RRepoGEO

REPOGEO REPORT · LITE

jingyaogong/minimind-o

Default branch master · commit f3a471b0 · scanned 6/17/2026, 6:22:48 PM

GitHub: 1,904 stars · 222 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface jingyaogong/minimind-o, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise English project summary to the main README's opening

    Why:

    CURRENT
    The current README excerpt starts with badges and a Chinese project introduction, with the English version linked separately.
    COPY-PASTE FIX
    Add the following English summary prominently at the very beginning of the main README (before any Chinese text or badges): "MiniMind-O is an open-source project dedicated to implementing a small-scale, end-to-end Omni model from scratch. It features a single weight capable of processing text, audio, and image inputs, and generating text and streaming speech outputs, making it one of the smallest complete Omni implementations available."
  • mediumtopics#2
    Refine repository topics for better categorization

    Why:

    CURRENT
    artificial-intelligence, chatgpt, omni
    COPY-PASTE FIX
    artificial-intelligence, multimodal-ai, omni-model, speech-recognition, text-to-speech, computer-vision, small-model, from-scratch-training
  • mediumreadme#3
    Add an explicit 'Problem Solved' section in English to the README

    Why:

    CURRENT
    The problem statement is present in the Chinese '项目介绍' section but not explicitly highlighted in English in the main README.
    COPY-PASTE FIX
    Add a section titled 'Problem Solved' or 'Motivation' to the README, containing text similar to: "While large-scale Omni models like GPT-4o and others offer advanced multimodal interaction, the open-source community lacks lightweight, end-to-end solutions for those aiming to understand, train, and modify a complete Omni model from scratch, rather than just using pre-trained weights. MiniMind-O addresses this gap by providing a minimal, fully implemented Omni model and training pipeline."

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface jingyaogong/minimind-o
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/peft · recommended 1×
  3. huggingface/diffusers · recommended 1×
  4. LLaVA · recommended 1×
  • CATEGORY QUERY
    How to train a small, end-to-end multimodal AI model with limited GPU resources?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. Diffusers (huggingface/diffusers)

    AI recommended 3 alternatives but never named jingyaogong/minimind-o. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open-source omnimodal AI model for local real-time audio and visual interaction.
    you: not recommended
    AI recommended (in order):
    1. LLaVA

    AI recommended 1 alternative but never named jingyaogong/minimind-o. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of jingyaogong/minimind-o?
    pass
    AI did not name jingyaogong/minimind-o — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts jingyaogong/minimind-o in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jingyaogong/minimind-o explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo jingyaogong/minimind-o solve, and who is the primary audience?
    pass
    AI did not name jingyaogong/minimind-o — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of jingyaogong/minimind-o. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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jingyaogong/minimind-o — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite